Time Series Forecasting

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139 papers in the last four weeks, up 101% on the four weeks before. 0.9% of all new papers.

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CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting
Sep 27, 2026Ya-Wen Wu, Meng-Fen Chiang, Kuang-Da Wang +1Financial Forecasting Time Series Forecasting
National Yang Ming Chiao Tung University, Hsinchu, Taiwan
BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables
Sep 27, 2026Siyu Gan, Dongsheng Luo, Kunxiaojia Yuan +2Spatiotemporal Forecasting Time Series Forecasting
Johns Hopkins University · Singapore Management University · University of Houston +1
Distributed Hydrological Modeling in the Feature Space
Sep 26, 2026Mohamad Hakam Shams Eddin, Maria Luisa Taccari, Yikui Zhang +3Flood Forecasting Time Series Forecasting
Max Planck Institute for Biogeochemistry, Germany · European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom · Institute of Bio- and Geosciences Agrosphere (IBG-3), Research Centre Jülich, Germany +2
Predicting the Financial Impact of Supply Chain Risk for Major AI-Related Semiconductor Firms: A Heterogeneous Graph Patch Transformer Approach
Sep 26, 2026Jianna Hur, Sagar SamtaniFinancial Forecasting Supply Chain Management
Data Science and Artificial Intelligence Lab (DSAIL), Kelley School of Business Indiana University, Bloomington, IN, USA
SIFT: Enhancing Time Series Foundation Models via Semantic Invariance and Structural Fidelity Fine-Tuning
Sep 26, 2026Yi Tang, Tengxue Zhang, Yang Shu +3Time-Series Foundation Models Time Series Forecasting
East China Normal University, Shanghai, China · Chang’an University, Xi’an, China
STRIDE: State-Transition Representation via Increment Dynamics and Evolution
Sep 26, 2026Yuchen Xiong, Siming Huang, Jianfeng SunTime Series Forecasting Probabilistic Time Series Forecasting
DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting
Sep 26, 2026Weiwei Ye, Dongyuan Li, Hangchen Liu +3Time Series Forecasting Probabilistic Time Series Forecasting
The University of Tokyo · Harbin Institute of Technology, Shenzhen
Model-Agnostic Online Certificate-Driven Calibration for Time Series Forecasting Under Distribution Shift
Sep 25, 2026Chenfeng Huang, Zixuan Ma, George MichailidisTime Series Forecasting OOD Generalization
Department of Statistics and Data Science, University of California, Los Angeles, Los Angeles, California, USA
UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting
Sep 25, 2026Derrick Gilchrist Edward Manoharan, Eljas Linna, Kestutis Baltakys +2Uncertainty Quantification Selective Prediction
Data Science Research Centre Tampere University Tampere, Finland
ViSTA: A Simple Bridge Extends Visual Alignment to Clinical Time-Series Understanding in Multimodal LLMs
Sep 25, 2026Junyi Gao, Yu Shi, Pingzhao Hu +1VLM Adaptation Medical VLMs
Centre for Medical Informatics, University of Edinburgh · Health Data Research UK · Biostatistics Division, Dalla Lana School of Public Health, University of Toronto +1
More Sensors Only One Field: Rethinking Continual Spatio-Temporal Forecasting
Sep 25, 2026Lewei Xie, Haoyu Zhang, Jiajun Zhou +6Spatiotemporal Forecasting Time Series Forecasting
City University of Hong Kong (Dongguan) · City University of Hong Kong · Zhuhai College of Science and Technology +1
WorldTS: World Modeling for Multimodal Covariate-aware Time Series Forecasting
Sep 25, 2026Yuhan Zhu, Xiangfei Qiu, Hanyin Cheng +5Time Series Forecasting Multivariate Time Series Forecasting
East China Normal University · Aalborg University
Aurora-X: Built for Extreme Time Series Forecasting
Sep 25, 2026Xingjian Wu, Chenjuan Guo, Xiangfei Qiu +6Time Series Forecasting Time-Series Foundation Models
East China Normal University
EPOC: Endpoint-Preserving Online Correction With Compressed Residual State for Multi-Horizon Time Series Forecasting
Sep 25, 2026Takumi Fujimoto, Hiroaki NishiMultivariate Time Series Forecasting Residual Learning
School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan · Department of System Design, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan
EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting
Sep 25, 2026Seunghan Lee, Sangjun Han, Jun Seo +9Time Series Forecasting Time-Series Foundation Models
LG AI Research
On the Limits of Univariate Deep Learning for Significant Wave Height Forecasting
Sep 25, 2026Yilin Zhai, Hongyuan Shi, Zaijin YouTime Series Forecasting Forecasting Benchmarks
Shandong Key Laboratory Of Estuary And Coast & Nuclear Environment, Ludong University, Shandong, 264025, Yantai, China · School of Hydraulic and Civil Engineering, Ludong University, Shandong, 264025, Yantai, China · Institute of Coastal Research, Ludong University, Yantai, 264025, Shandong, China +1
Neuralized Multi-Wavelet Decomposition for Time Series Classification and Forecasting
Sep 24, 2026Xiaohan Jiang, Jingyuan Wang, Jiahao Ji +3Time Series Classification Frequency-Domain Feature Learning
School of Computer Science and Engineering, Beihang University, Beijing, China · School of Computer Science and Engineering and the School of Economics and Management, Beihang University, Beijing, China · School of Computer Science and Engineering, Beihang University, Beijing, China, and are also with the MOE Engineering Research Center of Advanced Computer Application Technology, Beihang University, China +1
Downside-Controlled Online Forecast Combination under Delayed and Revised Outcomes
Sep 24, 2026Minkyoung Kim, Hyunjung Byun, Yohan Lee +1Time Series Forecasting Online Learning
Graduate School of Information, Yonsei University, Seoul, 03722, South Korea
The Cost of Long Memory: State, Context, and Stability Complexity in Sequence Models
Sep 24, 2026Yuheng SongTime Series Forecasting Recurrent Neural Networks
School of Management, Huazhong University of Science and Technology
Learning Collective Dynamics with Differentiable Gaussian Representations
Sep 23, 2026Jianxiang Ma, Mingfu Zhang, Xiaocui Yang +3Dynamical Systems Time Series Forecasting
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China · OranAI, Shenzhen 518057, China · OranAI Ltd., City of Industry, CA 91748, USA
fable.intermittent: benchmarking probabilistic forecasting methods for intermittent time series
Sep 23, 2026Stefano Damato, Lorenzo Zambon, Giorgio Corani +1Time Series Forecasting Forecasting Benchmarks
SUPSI, Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Lugano, Switzerland
SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models
Sep 23, 2026Xin-Yu Hu, Shuang Liang, Cheng Feng +1Uncertainty Quantification Time Series Forecasting
National Key Laboratory for Novel Software Technology, Nanjing University, China. · School of Intelligent Science and Technology, Nanjing University, China. · Siemens Data and AI Research, Beijing, China. +1
Time-Series Foundation Models That Understand Data Revisions
Sep 23, 2026Taimoor AhmadTime Series Forecasting Temporal Data Leakage
Superior University Lahore, Pakistan
Sparse-Observation Atmospheric Thermal Forecasting with Physics-Informed Neural Networks for Climate-Aware Digital Twins
Sep 23, 2026Tannaz Goodarzvand Chegini, Elyas Shivanian, Behzad Karimi +1Weather Forecasting Time Series Forecasting
Department of Mathematical Sciences / Montana State University, Bozeman, Montana, US · Department of Mathematics / Imam Khomeini International University, Qazvin, Iran · Department of Electrical and Computer Engineering, Montana State University, Bozeman, Montana, US +1
PROSWIN: Probabilistic Solar Wind Speed Forecasting Using Deep Distributional Regression From Solar Images
Sep 22, 2026Daniel Collin, Yuri Shprits, Luca Chiarabini +3Time Series Forecasting Probabilistic Time Series Forecasting
Space Physics and Space Weather, GFZ Helmholtz Centre for Geosciences, Potsdam, Germany · Department of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany · Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany +5
When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
Sep 21, 2026Yifan Hu, Xilin Dai, Zhiyuan Qu +4Time Series Forecasting Self-Evolving Agents
Ant International · Tsinghua University · The Chinese University of Hong Kong
TimeLitmus: A Diagnostic Benchmark for Cross-Modal Understanding and Explanation Faithfulness in Event-Conditioned Time-Series Prediction
Sep 21, 2026Jie Gong, Maowei Jiang, Zhiwei Liu +6LLM Evaluation LLM-Based Time Series Forecasting
School of Artificial Intelligence, Wuhan University, China · Nanjing Audit University, China · The University of Manchester, United Kingdom +3
GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting
Sep 21, 2026Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +1Time Series Forecasting Electricity Load Forecasting
CB · LMO, CELESTE · EDF R\&D +1
Overlay_dx - Automating forecasting evaluation
Sep 21, 2026Long Ngo, Mohammed Amine Chamli, Jonathan Rivalan +1Time Series Forecasting Forecasting Benchmarks
Smile, Asnières, France
$t_0$ : A Time-Series Foundation Model for Forecasting with Context
Sep 21, 2026Lucas Meyer, Claudio Sole, Huikan Xiang +6Multivariate Time Series Forecasting Zero-Shot Time Series Forecasting
The Forecasting Company · Macrocosm

Sep 27, 2026, cs.CLJ/K move · Enter open · S save

CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting

Ya-Wen Wu, Meng-Fen Chiang, Kuang-Da Wang, Wen-Chih Peng

National Yang Ming Chiao Tung University, Hsinchu, Taiwan

Financial Forecasting Time Series Forecasting Explainable Time Series Forecasting

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Quarter-ahead revenue forecasting requires company-scale numerical accuracy, strict temporal validity, and company-specific interpretation of narrative disclosures. LLMs can distill textual evidence but can produce scale-misaligned forecasts, whereas history-based anchors are stable but miss forecast-time signals such as product transitions, supply constraints, and management guidance. We introduce CAME (Company-Aware Evidence-Memory Experts), a residual-forecasting framework that refines a no-leakage statistical anchor when current semantic evidence and prior error patterns justify an adjustment. On a development-inclusive rolling backtest of 336 company-quarters from 12 large public technology and platform firms, CAME achieves the lowest aggregate point-estimate error among the reported methods, with statistically supported macro-sMAPE gains over the matched Statistical Anchor, and outperforms History + Guidance on all six aggregate metrics. CAME also links adjustments to source-linked evidence cards and guarded memory traces, supporting forecast inspection, provenance, and failure localization.